tests/testthat/test-power_MADE-options.R
In MikkelVembye/POMADE: Power for Meta-Analysis of Dependent Effects
pop_size <- 1000L
sigma2_emp <- rgamma(pop_size, shape = 5, rate = 10)
n_ES_emp <- 1 + rpois(pop_size, 5.5 - 1)
test_that("power_MADE() works with single parameter values, averaged power.", {
expect_warning(
res <- power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = TRUE
)
)
expect_equal(nrow(res), 1L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = TRUE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(2L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 7
) %>%
nrow() %>%
expect_equal(21L)
})
test_that("power_MADE() works with single parameter values, raw power.", {
expect_warning(
res <- power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = FALSE
)
)
expect_equal(nrow(res), 1L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = FALSE,
iterations = 10,
warning = FALSE
) %>%
nrow() %>%
expect_equal(20L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 4
) %>%
nrow() %>%
expect_equal(12L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 7,
warning = FALSE
) %>%
nrow() %>%
expect_equal(21L * 7L)
})
test_that("power_MADE() works with multiple parameter values, averaged power.", {
skip_on_cran()
power_MADE(
J = c(10,20,40),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.3,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = TRUE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = c(0.1, 0.2, 0.3),
omega = 0.1,
rho = c(0.2,0.7),
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = TRUE,
iterations = 5,
warning = FALSE
) %>%
nrow() %>%
expect_equal(12L)
power_MADE(
J = seq(10,30,10),
mu = seq(0.1,0.6,0.1),
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L * 3L * 6L)
power_MADE(
J = c(20,40),
mu = seq(0.0,0.8,0.2),
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 2
) %>%
nrow() %>%
expect_equal(2L * 3L * 2L * 5L * 3L * 4L * 3L)
})
test_that("power_MADE() works with multiple parameter values, raw power.", {
skip_on_cran()
power_MADE(
J = c(10,20,40),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.3,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = FALSE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = c(0.1, 0.2, 0.3),
omega = 0.1,
rho = c(0.2,0.7),
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = FALSE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(12L * 100L)
power_MADE(
J = seq(10,30,10),
mu = seq(0.1,0.6,0.1),
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L * 3L * 6L * 4L)
power_MADE(
J = c(20,40),
mu = seq(0.0,0.8,0.2),
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 3
) %>%
nrow() %>%
expect_equal(2L * 3L * 2L * 5L * 3L * 4L * 3L * 3L)
})
test_that("power_MADE() returns alpha when null is true.", {
res_RVE <-
power_MADE(
J = c(20,40),
mu = 0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA","CE"),
var_df = "RVE",
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
iterations = 3
)
expect_equal(res_RVE$alpha, res_RVE$power)
res_Satt <-
power_MADE(
J = c(20,40),
mu = 0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = "CHE",
var_df = "Satt",
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
iterations = 3,
warning = FALSE
)
expect_equal(res_Satt$alpha, res_Satt$power)
res_balanced <-
power_MADE(
J = c(20,40),
mu = 0.0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = 4 / 48,
n_ES_dist = 3,
model = c("CHE","MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = seq(0.04, 0.40, length.out = 9),
average_power = FALSE,
iterations = 3,
warning = FALSE
)
expect_equal(res_balanced$alpha, res_balanced$power, tolerance = 5e-5)
})
test_that("power_MADE() returns constant power for balanced designs.", {
res <-
power_MADE(
J = c(20,40),
mu = 0.07,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = 4 / 48,
n_ES_dist = 2,
model = c("CHE", "MLMA","CE"),
var_df = c("Model","Satt","RVE"),
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
warning = FALSE
)
res_comparison <-
res %>%
group_by(J, mu, tau, omega, rho, alpha) %>%
summarise(
models = n(),
across(c(power,var_b, df), ~ diff(range(.x))),
.groups = "drop"
)
expect_lt(max(res_comparison$power), 1e-5)
expect_lt(max(res_comparison$var_b), 1e-5)
expect_lt(max(res_comparison$df), 1e-5)
})
MikkelVembye/POMADE documentation built on June 15, 2024, 7:15 p.m.
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pop_size <- 1000L
sigma2_emp <- rgamma(pop_size, shape = 5, rate = 10)
n_ES_emp <- 1 + rpois(pop_size, 5.5 - 1)
test_that("power_MADE() works with single parameter values, averaged power.", {
expect_warning(
res <- power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = TRUE
)
)
expect_equal(nrow(res), 1L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = TRUE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(2L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 7
) %>%
nrow() %>%
expect_equal(21L)
})
test_that("power_MADE() works with single parameter values, raw power.", {
expect_warning(
res <- power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = FALSE
)
)
expect_equal(nrow(res), 1L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = FALSE,
iterations = 10,
warning = FALSE
) %>%
nrow() %>%
expect_equal(20L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 4
) %>%
nrow() %>%
expect_equal(12L)
power_MADE(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 7,
warning = FALSE
) %>%
nrow() %>%
expect_equal(21L * 7L)
})
test_that("power_MADE() works with multiple parameter values, averaged power.", {
skip_on_cran()
power_MADE(
J = c(10,20,40),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.3,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = TRUE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = c(0.1, 0.2, 0.3),
omega = 0.1,
rho = c(0.2,0.7),
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = TRUE,
iterations = 5,
warning = FALSE
) %>%
nrow() %>%
expect_equal(12L)
power_MADE(
J = seq(10,30,10),
mu = seq(0.1,0.6,0.1),
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L * 3L * 6L)
power_MADE(
J = c(20,40),
mu = seq(0.0,0.8,0.2),
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = TRUE,
iterations = 2
) %>%
nrow() %>%
expect_equal(2L * 3L * 2L * 5L * 3L * 4L * 3L)
})
test_that("power_MADE() works with multiple parameter values, raw power.", {
skip_on_cran()
power_MADE(
J = c(10,20,40),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.3,
sigma2_dist = 4 / 100,
n_ES_dist = 5.5,
model = "CHE",
var_df = "Satt",
alpha = 0.05,
average_power = FALSE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(3L)
power_MADE(
J = 40,
mu = 0.1,
tau = c(0.1, 0.2, 0.3),
omega = 0.1,
rho = c(0.2,0.7),
sigma2_dist = 4 / 100,
n_ES_dist = n_ES_emp,
model = c("CHE","MLMA"),
alpha = 0.05,
average_power = FALSE,
warning = FALSE
) %>%
nrow() %>%
expect_equal(12L * 100L)
power_MADE(
J = seq(10,30,10),
mu = seq(0.1,0.6,0.1),
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_emp,
n_ES_dist = n_ES_emp,
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 4
) %>%
nrow() %>%
expect_equal(3L * 3L * 6L * 4L)
power_MADE(
J = c(20,40),
mu = seq(0.0,0.8,0.2),
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = c(.01, 0.025, .1),
average_power = FALSE,
iterations = 3
) %>%
nrow() %>%
expect_equal(2L * 3L * 2L * 5L * 3L * 4L * 3L * 3L)
})
test_that("power_MADE() returns alpha when null is true.", {
res_RVE <-
power_MADE(
J = c(20,40),
mu = 0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = c("CHE", "MLMA","CE"),
var_df = "RVE",
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
iterations = 3
)
expect_equal(res_RVE$alpha, res_RVE$power)
res_Satt <-
power_MADE(
J = c(20,40),
mu = 0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + rpois(x, 5.5 - 1),
model = "CHE",
var_df = "Satt",
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
iterations = 3,
warning = FALSE
)
expect_equal(res_Satt$alpha, res_Satt$power)
res_balanced <-
power_MADE(
J = c(20,40),
mu = 0.0,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = 4 / 48,
n_ES_dist = 3,
model = c("CHE","MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = seq(0.04, 0.40, length.out = 9),
average_power = FALSE,
iterations = 3,
warning = FALSE
)
expect_equal(res_balanced$alpha, res_balanced$power, tolerance = 5e-5)
})
test_that("power_MADE() returns constant power for balanced designs.", {
res <-
power_MADE(
J = c(20,40),
mu = 0.07,
tau = c(0.1, 0.2, 0.3),
omega = c(0.1, 0.2),
rho = c(0.4,0.7,0.9),
sigma2_dist = 4 / 48,
n_ES_dist = 2,
model = c("CHE", "MLMA","CE"),
var_df = c("Model","Satt","RVE"),
alpha = seq(.01, 0.4, length.out = 10),
average_power = FALSE,
warning = FALSE
)
res_comparison <-
res %>%
group_by(J, mu, tau, omega, rho, alpha) %>%
summarise(
models = n(),
across(c(power,var_b, df), ~ diff(range(.x))),
.groups = "drop"
)
expect_lt(max(res_comparison$power), 1e-5)
expect_lt(max(res_comparison$var_b), 1e-5)
expect_lt(max(res_comparison$df), 1e-5)
})
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